Overview

Brought to you by YData

Dataset statistics

Number of variables29
Number of observations2557656
Missing cells38450611
Missing cells (%)51.8%
Duplicate rows253
Duplicate rows (%)< 0.1%
Total size in memory565.9 MiB
Average record size in memory232.0 B

Variable types

Text2
DateTime1
Numeric22
Categorical4

Alerts

Dataset has 253 (< 0.1%) duplicate rowsDuplicates
estado is highly overall correlated with resultado_del_ensayo and 9 other fieldsHigh correlation
estimulacion is highly overall correlated with memory_[%] and 1 other fieldsHigh correlation
gp_[mmm3] is highly overall correlated with resultado_del_ensayo and 2 other fieldsHigh correlation
h_punzado is highly overall correlated with test_ptub_[kg/cm2]High correlation
lp_[mm3] is highly overall correlated with np_[mm3] and 2 other fieldsHigh correlation
memory_[%] is highly overall correlated with estimulacion and 1 other fieldsHigh correlation
np_[mm3] is highly overall correlated with lp_[mm3] and 1 other fieldsHigh correlation
qg_[m3/dc] is highly overall correlated with ql_[m3/dc] and 1 other fieldsHigh correlation
ql_[m3/dc] is highly overall correlated with qg_[m3/dc] and 2 other fieldsHigh correlation
qo_[m3/dc] is highly overall correlated with ql_[m3/dc] and 1 other fieldsHigh correlation
qw_[m3/dc] is highly overall correlated with resultado_del_ensayoHigh correlation
qwi_[m3/dc] is highly overall correlated with resultado_del_ensayoHigh correlation
resultado_del_ensayo is highly overall correlated with estado and 10 other fieldsHigh correlation
sft_[kg/cm2] is highly overall correlated with estado and 1 other fieldsHigh correlation
test_caudal_de_agua_[l/h] is highly overall correlated with estado and 4 other fieldsHigh correlation
test_caudal_de_liquido_[l/h] is highly overall correlated with estado and 5 other fieldsHigh correlation
test_caudal_de_petroleo_[l/h] is highly overall correlated with estado and 2 other fieldsHigh correlation
test_nivel_[m tvd] is highly overall correlated with estado and 4 other fieldsHigh correlation
test_porcentaje_de_agua_[%] is highly overall correlated with estado and 2 other fieldsHigh correlation
test_presion_de_inyeccion_[kg/cm2] is highly overall correlated with estadoHigh correlation
test_ptub_[kg/cm2] is highly overall correlated with h_punzado and 7 other fieldsHigh correlation
test_pws_[kg/cm2] is highly overall correlated with memory_[%] and 4 other fieldsHigh correlation
test_qwi_[l/h] is highly overall correlated with estado and 1 other fieldsHigh correlation
test_salinidad_[g/cm3] is highly overall correlated with estado and 2 other fieldsHigh correlation
wi_[mm3] is highly overall correlated with gp_[mmm3] and 1 other fieldsHigh correlation
wp_[mm3] is highly overall correlated with lp_[mm3] and 1 other fieldsHigh correlation
estado is highly imbalanced (56.9%)Imbalance
estimulacion is highly imbalanced (99.0%)Imbalance
estado has 2553249 (99.8%) missing valuesMissing
resultado_del_ensayo has 2551416 (99.8%) missing valuesMissing
test_caudal_de_petroleo_[l/h] has 2555186 (99.9%) missing valuesMissing
test_caudal_de_agua_[l/h] has 2555186 (99.9%) missing valuesMissing
test_caudal_de_liquido_[l/h] has 2554075 (99.9%) missing valuesMissing
test_qwi_[l/h] has 2556686 (> 99.9%) missing valuesMissing
test_porcentaje_de_agua_[%] has 2555186 (99.9%) missing valuesMissing
test_nivel_[m tvd] has 2554153 (99.9%) missing valuesMissing
test_salinidad_[g/cm3] has 2555749 (99.9%) missing valuesMissing
test_pws_[kg/cm2] has 2557003 (> 99.9%) missing valuesMissing
test_ptub_[kg/cm2] has 2557647 (> 99.9%) missing valuesMissing
test_presion_de_inyeccion_[kg/cm2] has 2556682 (> 99.9%) missing valuesMissing
memory_[%] has 2557391 (> 99.9%) missing valuesMissing
h_punzado has 2552348 (99.8%) missing valuesMissing
sft_[kg/cm2] has 2556680 (> 99.9%) missing valuesMissing
qg_[m3/dc] is highly skewed (γ1 = 54.44908403)Skewed
qo_[m3/dc] has 2232608 (87.3%) zerosZeros
np_[mm3] has 1449652 (56.7%) zerosZeros
qg_[m3/dc] has 2136195 (83.5%) zerosZeros
gp_[mmm3] has 519391 (20.3%) zerosZeros
qw_[m3/dc] has 2469939 (96.6%) zerosZeros
wp_[mm3] has 2238328 (87.5%) zerosZeros
ql_[m3/dc] has 2212498 (86.5%) zerosZeros
lp_[mm3] has 1355481 (53.0%) zerosZeros
qwi_[m3/dc] has 2457763 (96.1%) zerosZeros
wi_[mm3] has 2036417 (79.6%) zerosZeros

Reproduction

Analysis started2024-08-26 16:17:30.650095
Analysis finished2024-08-26 16:19:08.352305
Duration1 minute and 37.7 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

Distinct189
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.5 MiB
2024-08-26T18:19:08.475248image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length11
Median length8
Mean length7.7324382
Min length5

Characters and Unicode

Total characters19776917
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPLM-33
2nd rowPLM-33
3rd rowPLM-33
4th rowPLM-33
5th rowPLM-33
ValueCountFrequency (%)
plms-703 34872
 
1.4%
plms-886 29753
 
1.2%
plms-809 29038
 
1.1%
plms-9 28438
 
1.1%
plms-808 28345
 
1.1%
plms-801 28185
 
1.1%
plms-806 28171
 
1.1%
plms-816 28133
 
1.1%
plms-30 28075
 
1.1%
plms-810 27375
 
1.1%
Other values (172) 2267271
88.6%
2024-08-26T18:19:08.679290image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
P 2557656
12.9%
M 2557656
12.9%
- 2557656
12.9%
L 2557656
12.9%
S 2291532
11.6%
8 2001083
10.1%
9 980241
 
5.0%
0 690464
 
3.5%
2 654056
 
3.3%
1 631869
 
3.2%
Other values (14) 2297048
11.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19776917
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 2557656
12.9%
M 2557656
12.9%
- 2557656
12.9%
L 2557656
12.9%
S 2291532
11.6%
8 2001083
10.1%
9 980241
 
5.0%
0 690464
 
3.5%
2 654056
 
3.3%
1 631869
 
3.2%
Other values (14) 2297048
11.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19776917
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 2557656
12.9%
M 2557656
12.9%
- 2557656
12.9%
L 2557656
12.9%
S 2291532
11.6%
8 2001083
10.1%
9 980241
 
5.0%
0 690464
 
3.5%
2 654056
 
3.3%
1 631869
 
3.2%
Other values (14) 2297048
11.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19776917
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 2557656
12.9%
M 2557656
12.9%
- 2557656
12.9%
L 2557656
12.9%
S 2291532
11.6%
8 2001083
10.1%
9 980241
 
5.0%
0 690464
 
3.5%
2 654056
 
3.3%
1 631869
 
3.2%
Other values (14) 2297048
11.6%

capa
Text

Distinct206
Distinct (%)< 0.1%
Missing618
Missing (%)< 0.1%
Memory size19.5 MiB
2024-08-26T18:19:08.817290image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length18
Median length17
Mean length3.9035005
Min length2

Characters and Unicode

Total characters9981399
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)< 0.1%

Sample

1st rowF-9
2nd rowF-9
3rd rowF-9
4th rowF-9
5th rowF-9
ValueCountFrequency (%)
yacimiento 124671
 
4.9%
j-1bc 89623
 
3.5%
j-1bb 80189
 
3.1%
f-7a 79643
 
3.1%
g-6ab 76533
 
3.0%
l-2 69861
 
2.7%
i-1b 69431
 
2.7%
c-8b 65390
 
2.6%
h-7 59522
 
2.3%
h-4 49234
 
1.9%
Other values (128) 1793556
70.1%
2024-08-26T18:19:09.028291image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 2394711
24.0%
B 704536
 
7.1%
A 614334
 
6.2%
1 451246
 
4.5%
J 391366
 
3.9%
6 367288
 
3.7%
2 355405
 
3.6%
I 332962
 
3.3%
H 299510
 
3.0%
4 298037
 
3.0%
Other values (27) 3772004
37.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9981399
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 2394711
24.0%
B 704536
 
7.1%
A 614334
 
6.2%
1 451246
 
4.5%
J 391366
 
3.9%
6 367288
 
3.7%
2 355405
 
3.6%
I 332962
 
3.3%
H 299510
 
3.0%
4 298037
 
3.0%
Other values (27) 3772004
37.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9981399
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 2394711
24.0%
B 704536
 
7.1%
A 614334
 
6.2%
1 451246
 
4.5%
J 391366
 
3.9%
6 367288
 
3.7%
2 355405
 
3.6%
I 332962
 
3.3%
H 299510
 
3.0%
4 298037
 
3.0%
Other values (27) 3772004
37.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9981399
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 2394711
24.0%
B 704536
 
7.1%
A 614334
 
6.2%
1 451246
 
4.5%
J 391366
 
3.9%
6 367288
 
3.7%
2 355405
 
3.6%
I 332962
 
3.3%
H 299510
 
3.0%
4 298037
 
3.0%
Other values (27) 3772004
37.8%

fecha
Date

Distinct1688
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.5 MiB
Minimum1960-12-01 00:00:00
Maximum2060-12-01 00:00:00
2024-08-26T18:19:09.102289image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:19:09.177306image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

qo_[m3/dc]
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct49357
Distinct (%)1.9%
Missing11857
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean0.15518231
Minimum-7.3069
Maximum100.8064
Zeros2232608
Zeros (%)87.3%
Negative197
Negative (%)< 0.1%
Memory size19.5 MiB
2024-08-26T18:19:09.259305image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum-7.3069
5-th percentile0
Q10
median0
Q30
95-th percentile0.5695
Maximum100.8064
Range108.1133
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0979002
Coefficient of variation (CV)7.0749056
Kurtosis639.73302
Mean0.15518231
Median Absolute Deviation (MAD)0
Skewness18.975893
Sum395062.98
Variance1.2053849
MonotonicityNot monotonic
2024-08-26T18:19:09.328305image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2232608
87.3%
0.0384 79
 
< 0.1%
0.0287 79
 
< 0.1%
0.0078 76
 
< 0.1%
0.0363 75
 
< 0.1%
0.0216 75
 
< 0.1%
0.0425 74
 
< 0.1%
0.046 73
 
< 0.1%
0.054 72
 
< 0.1%
0.0146 72
 
< 0.1%
Other values (49347) 312516
 
12.2%
(Missing) 11857
 
0.5%
ValueCountFrequency (%)
-7.3069 1
< 0.1%
-7.2078 1
< 0.1%
-6.3415 1
< 0.1%
-6.259 1
< 0.1%
-5.2595 1
< 0.1%
-5.0906 1
< 0.1%
-5.0584 1
< 0.1%
-4.922 1
< 0.1%
-4.5032 1
< 0.1%
-4.4992 1
< 0.1%
ValueCountFrequency (%)
100.8064 1
< 0.1%
94.1613 1
< 0.1%
86.9355 1
< 0.1%
84.8214 1
< 0.1%
82.3 1
< 0.1%
82.0645 1
< 0.1%
78.3333 1
< 0.1%
76.7 1
< 0.1%
74.4839 1
< 0.1%
73.6774 1
< 0.1%

np_[mm3]
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct41193
Distinct (%)1.6%
Missing11857
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean3.3199125
Minimum-8.389
Maximum304.637
Zeros1449652
Zeros (%)56.7%
Negative647
Negative (%)< 0.1%
Memory size19.5 MiB
2024-08-26T18:19:09.395305image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum-8.389
5-th percentile0
Q10
median0
Q31.502
95-th percentile15.745
Maximum304.637
Range313.026
Interquartile range (IQR)1.502

Descriptive statistics

Standard deviation13.255998
Coefficient of variation (CV)3.9928756
Kurtosis166.1521
Mean3.3199125
Median Absolute Deviation (MAD)0
Skewness10.625344
Sum8451830
Variance175.72147
MonotonicityNot monotonic
2024-08-26T18:19:09.463907image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1449652
56.7%
0.524 2897
 
0.1%
2.815 2173
 
0.1%
0.962 2108
 
0.1%
0.101 1955
 
0.1%
1.363 1948
 
0.1%
0.478 1944
 
0.1%
0.862 1902
 
0.1%
1.503 1891
 
0.1%
1.241 1885
 
0.1%
Other values (41183) 1077444
42.1%
(Missing) 11857
 
0.5%
ValueCountFrequency (%)
-8.389 451
< 0.1%
-8.374 1
 
< 0.1%
-8.361 1
 
< 0.1%
-8.349 1
 
< 0.1%
-8.337 1
 
< 0.1%
-8.326 1
 
< 0.1%
-8.311 1
 
< 0.1%
-8.296 1
 
< 0.1%
-8.282 1
 
< 0.1%
-8.266 1
 
< 0.1%
ValueCountFrequency (%)
304.637 451
< 0.1%
304.486 1
 
< 0.1%
304.339 1
 
< 0.1%
304.204 1
 
< 0.1%
304.03 1
 
< 0.1%
303.872 1
 
< 0.1%
303.7 1
 
< 0.1%
303.537 1
 
< 0.1%
303.378 1
 
< 0.1%
303.239 1
 
< 0.1%

qg_[m3/dc]
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct147942
Distinct (%)5.8%
Missing11857
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean1.7934464
Minimum0
Maximum5958.962
Zeros2136195
Zeros (%)83.5%
Negative0
Negative (%)0.0%
Memory size19.5 MiB
2024-08-26T18:19:09.531908image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6.1789
Maximum5958.962
Range5958.962
Interquartile range (IQR)0

Descriptive statistics

Standard deviation18.296846
Coefficient of variation (CV)10.202059
Kurtosis9564.79
Mean1.7934464
Median Absolute Deviation (MAD)0
Skewness54.449084
Sum4565754.2
Variance334.77456
MonotonicityNot monotonic
2024-08-26T18:19:09.606907image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2136195
83.5%
0.0001 166
 
< 0.1%
64.5161 154
 
< 0.1%
0.0003 130
 
< 0.1%
129.0323 120
 
< 0.1%
0.0002 116
 
< 0.1%
96.7742 98
 
< 0.1%
193.5484 93
 
< 0.1%
0.0005 85
 
< 0.1%
0.0004 84
 
< 0.1%
Other values (147932) 408558
 
16.0%
(Missing) 11857
 
0.5%
ValueCountFrequency (%)
0 2136195
83.5%
0.0001 166
 
< 0.1%
0.0002 116
 
< 0.1%
0.0003 130
 
< 0.1%
0.0004 84
 
< 0.1%
0.0005 85
 
< 0.1%
0.0006 74
 
< 0.1%
0.0007 65
 
< 0.1%
0.0008 65
 
< 0.1%
0.0009 71
 
< 0.1%
ValueCountFrequency (%)
5958.962 1
< 0.1%
5641.876 1
< 0.1%
3024.324 1
< 0.1%
1932.364 1
< 0.1%
1757.605 1
< 0.1%
1719.991 1
< 0.1%
1707.607 1
< 0.1%
1691.296 1
< 0.1%
1640 1
< 0.1%
1631.834 1
< 0.1%

gp_[mmm3]
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2134
Distinct (%)0.1%
Missing11857
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean0.032273765
Minimum-0.476
Maximum2.743
Zeros519391
Zeros (%)20.3%
Negative826
Negative (%)< 0.1%
Memory size19.5 MiB
2024-08-26T18:19:09.676907image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum-0.476
5-th percentile0
Q10.002
median0.01
Q30.024
95-th percentile0.085
Maximum2.743
Range3.219
Interquartile range (IQR)0.022

Descriptive statistics

Standard deviation0.12429865
Coefficient of variation (CV)3.8513836
Kurtosis168.4747
Mean0.032273765
Median Absolute Deviation (MAD)0.01
Skewness11.296346
Sum82162.519
Variance0.015450155
MonotonicityNot monotonic
2024-08-26T18:19:09.747907image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 519391
 
20.3%
0.001 90044
 
3.5%
0.002 88717
 
3.5%
0.003 85791
 
3.4%
0.004 85289
 
3.3%
0.005 82839
 
3.2%
0.006 78675
 
3.1%
0.009 70349
 
2.8%
0.007 69761
 
2.7%
0.008 69487
 
2.7%
Other values (2124) 1305456
51.0%
ValueCountFrequency (%)
-0.476 1
< 0.1%
-0.474 1
< 0.1%
-0.471 1
< 0.1%
-0.469 1
< 0.1%
-0.467 1
< 0.1%
-0.465 1
< 0.1%
-0.462 1
< 0.1%
-0.46 1
< 0.1%
-0.459 1
< 0.1%
-0.453 1
< 0.1%
ValueCountFrequency (%)
2.743 451
< 0.1%
2.738 1
 
< 0.1%
2.735 1
 
< 0.1%
2.734 1
 
< 0.1%
2.732 2
 
< 0.1%
2.731 1
 
< 0.1%
2.729 1
 
< 0.1%
2.727 1
 
< 0.1%
2.725 1
 
< 0.1%
2.722 1
 
< 0.1%

qw_[m3/dc]
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct57942
Distinct (%)2.3%
Missing11857
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean0.85968647
Minimum0
Maximum453.1391
Zeros2469939
Zeros (%)96.6%
Negative0
Negative (%)0.0%
Memory size19.5 MiB
2024-08-26T18:19:09.819907image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum453.1391
Range453.1391
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10.140068
Coefficient of variation (CV)11.795077
Kurtosis353.87585
Mean0.85968647
Median Absolute Deviation (MAD)0
Skewness16.913352
Sum2188589
Variance102.82099
MonotonicityNot monotonic
2024-08-26T18:19:09.891907image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2469939
96.6%
3 50
 
< 0.1%
1.4194 48
 
< 0.1%
1.5806 45
 
< 0.1%
1.7419 45
 
< 0.1%
1.2581 44
 
< 0.1%
1.9032 43
 
< 0.1%
0.7742 38
 
< 0.1%
2.2258 37
 
< 0.1%
2.0323 36
 
< 0.1%
Other values (57932) 75474
 
3.0%
(Missing) 11857
 
0.5%
ValueCountFrequency (%)
0 2469939
96.6%
0.0001 1
 
< 0.1%
0.0021 1
 
< 0.1%
0.0035 1
 
< 0.1%
0.0046 1
 
< 0.1%
0.0067 1
 
< 0.1%
0.0099 1
 
< 0.1%
0.0137 1
 
< 0.1%
0.0143 1
 
< 0.1%
0.0144 1
 
< 0.1%
ValueCountFrequency (%)
453.1391 1
< 0.1%
450.9763 1
< 0.1%
449.6364 1
< 0.1%
449.0609 1
< 0.1%
448.12 1
< 0.1%
444.8892 1
< 0.1%
442.8363 1
< 0.1%
439.0512 1
< 0.1%
435.4138 1
< 0.1%
435.3819 1
< 0.1%

wp_[mm3]
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct42581
Distinct (%)1.7%
Missing11857
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean14.21869
Minimum-118.262
Maximum2127.764
Zeros2238328
Zeros (%)87.5%
Negative3570
Negative (%)0.1%
Memory size19.5 MiB
2024-08-26T18:19:09.958907image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum-118.262
5-th percentile0
Q10
median0
Q30
95-th percentile19.816
Maximum2127.764
Range2246.026
Interquartile range (IQR)0

Descriptive statistics

Standard deviation97.928804
Coefficient of variation (CV)6.8873294
Kurtosis148.25025
Mean14.21869
Median Absolute Deviation (MAD)0
Skewness10.888675
Sum36197927
Variance9590.0506
MonotonicityNot monotonic
2024-08-26T18:19:10.026907image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2238328
87.5%
7.144 1839
 
0.1%
8.113 980
 
< 0.1%
1.365 925
 
< 0.1%
1.786 925
 
< 0.1%
4.217 923
 
< 0.1%
2.163 922
 
< 0.1%
6.072 921
 
< 0.1%
3.786 920
 
< 0.1%
21.431 918
 
< 0.1%
Other values (42571) 298198
 
11.7%
(Missing) 11857
 
0.5%
ValueCountFrequency (%)
-118.262 1
< 0.1%
-117.415 1
< 0.1%
-115.965 1
< 0.1%
-114.761 1
< 0.1%
-113.991 1
< 0.1%
-112.083 1
< 0.1%
-110.427 1
< 0.1%
-109.293 1
< 0.1%
-108.415 1
< 0.1%
-106.873 1
< 0.1%
ValueCountFrequency (%)
2127.764 451
< 0.1%
2120.077 1
 
< 0.1%
2112.088 1
 
< 0.1%
2104.37 1
 
< 0.1%
2096.086 1
 
< 0.1%
2089.447 1
 
< 0.1%
2081.737 1
 
< 0.1%
2073.249 1
 
< 0.1%
2064.867 1
 
< 0.1%
2057.123 1
 
< 0.1%

ql_[m3/dc]
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct74851
Distinct (%)2.9%
Missing11857
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean1.0148688
Minimum-7.3069
Maximum462.1085
Zeros2212498
Zeros (%)86.5%
Negative197
Negative (%)< 0.1%
Memory size19.5 MiB
2024-08-26T18:19:10.091907image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum-7.3069
5-th percentile0
Q10
median0
Q30
95-th percentile0.8239
Maximum462.1085
Range469.4154
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10.693809
Coefficient of variation (CV)10.537135
Kurtosis326.99598
Mean1.0148688
Median Absolute Deviation (MAD)0
Skewness16.237507
Sum2583651.9
Variance114.35756
MonotonicityNot monotonic
2024-08-26T18:19:10.161907image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2212498
86.5%
0.0078 75
 
< 0.1%
0.0363 74
 
< 0.1%
0.0384 74
 
< 0.1%
0.0287 72
 
< 0.1%
0.0216 72
 
< 0.1%
0.0053 70
 
< 0.1%
0.054 70
 
< 0.1%
0.0055 69
 
< 0.1%
0.1063 69
 
< 0.1%
Other values (74841) 332656
 
13.0%
(Missing) 11857
 
0.5%
ValueCountFrequency (%)
-7.3069 1
< 0.1%
-7.2078 1
< 0.1%
-6.3415 1
< 0.1%
-6.259 1
< 0.1%
-5.2595 1
< 0.1%
-5.0906 1
< 0.1%
-5.0584 1
< 0.1%
-4.922 1
< 0.1%
-4.5032 1
< 0.1%
-4.4992 1
< 0.1%
ValueCountFrequency (%)
462.1085 1
< 0.1%
458.6851 1
< 0.1%
458.3124 1
< 0.1%
458.2317 1
< 0.1%
456.7583 1
< 0.1%
454.3044 1
< 0.1%
451.625 1
< 0.1%
448.1599 1
< 0.1%
444.1711 1
< 0.1%
443.6735 1
< 0.1%

lp_[mm3]
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct55687
Distinct (%)2.2%
Missing11857
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean17.5386
Minimum-117.573
Maximum2303.667
Zeros1355481
Zeros (%)53.0%
Negative1384
Negative (%)0.1%
Memory size19.5 MiB
2024-08-26T18:19:10.226842image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum-117.573
5-th percentile0
Q10
median0
Q32.246
95-th percentile33.104
Maximum2303.667
Range2421.24
Interquartile range (IQR)2.246

Descriptive statistics

Standard deviation107.48191
Coefficient of variation (CV)6.1283061
Kurtosis144.97504
Mean17.5386
Median Absolute Deviation (MAD)0
Skewness10.766385
Sum44649750
Variance11552.36
MonotonicityNot monotonic
2024-08-26T18:19:10.295843image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1355481
53.0%
0.524 2898
 
0.1%
2.815 2175
 
0.1%
0.962 2101
 
0.1%
1.363 1946
 
0.1%
0.478 1943
 
0.1%
0.862 1903
 
0.1%
1.503 1888
 
0.1%
0.393 1816
 
0.1%
0.056 1677
 
0.1%
Other values (55677) 1171971
45.8%
(Missing) 11857
 
0.5%
ValueCountFrequency (%)
-117.573 1
< 0.1%
-115.068 1
< 0.1%
-112.031 1
< 0.1%
-109.737 1
< 0.1%
-108.125 1
< 0.1%
-104.98 1
< 0.1%
-102.512 1
< 0.1%
-99.809 1
< 0.1%
-98.109 1
< 0.1%
-95.059 1
< 0.1%
ValueCountFrequency (%)
2303.667 451
< 0.1%
2295.858 1
 
< 0.1%
2287.731 1
 
< 0.1%
2279.89 1
 
< 0.1%
2271.49 1
 
< 0.1%
2264.727 1
 
< 0.1%
2256.872 1
 
< 0.1%
2248.234 1
 
< 0.1%
2239.72 1
 
< 0.1%
2231.831 1
 
< 0.1%

qwi_[m3/dc]
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct77226
Distinct (%)3.0%
Missing11857
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean1.8047034
Minimum0
Maximum975.6774
Zeros2457763
Zeros (%)96.1%
Negative0
Negative (%)0.0%
Memory size19.5 MiB
2024-08-26T18:19:10.363842image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum975.6774
Range975.6774
Interquartile range (IQR)0

Descriptive statistics

Standard deviation19.953121
Coefficient of variation (CV)11.056177
Kurtosis461.62895
Mean1.8047034
Median Absolute Deviation (MAD)0
Skewness19.896373
Sum4594412
Variance398.12702
MonotonicityNot monotonic
2024-08-26T18:19:10.434843image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2457763
96.1%
32.091 12
 
< 0.1%
9.975 12
 
< 0.1%
5.985 9
 
< 0.1%
32.832 7
 
< 0.1%
28.842 7
 
< 0.1%
1.767 7
 
< 0.1%
26.484 7
 
< 0.1%
38.19 7
 
< 0.1%
72.618 6
 
< 0.1%
Other values (77216) 87962
 
3.4%
(Missing) 11857
 
0.5%
ValueCountFrequency (%)
0 2457763
96.1%
0.0009 1
 
< 0.1%
0.0013 1
 
< 0.1%
0.0014 1
 
< 0.1%
0.0019 1
 
< 0.1%
0.002 1
 
< 0.1%
0.0021 1
 
< 0.1%
0.0025 1
 
< 0.1%
0.0026 1
 
< 0.1%
0.0027 2
 
< 0.1%
ValueCountFrequency (%)
975.6774 1
< 0.1%
836.2581 1
< 0.1%
813 1
< 0.1%
804.5484 1
< 0.1%
790.5806 1
< 0.1%
788 1
< 0.1%
780.7097 1
< 0.1%
772.5 1
< 0.1%
761.3226 1
< 0.1%
760.931 1
< 0.1%

wi_[mm3]
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct66366
Distinct (%)2.6%
Missing11857
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean30.363723
Minimum0
Maximum4538.833
Zeros2036417
Zeros (%)79.6%
Negative0
Negative (%)0.0%
Memory size19.5 MiB
2024-08-26T18:19:10.501737image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile126.414
Maximum4538.833
Range4538.833
Interquartile range (IQR)0

Descriptive statistics

Standard deviation180.14926
Coefficient of variation (CV)5.9330423
Kurtosis224.35443
Mean30.363723
Median Absolute Deviation (MAD)0
Skewness13.317114
Sum77299937
Variance32453.754
MonotonicityNot monotonic
2024-08-26T18:19:10.567197image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2036417
79.6%
0.132 1031
 
< 0.1%
20.461 983
 
< 0.1%
9.178 902
 
< 0.1%
12.556 902
 
< 0.1%
1.442 805
 
< 0.1%
0.344 712
 
< 0.1%
166.65 691
 
< 0.1%
103.503 676
 
< 0.1%
14.308 673
 
< 0.1%
Other values (66356) 502007
 
19.6%
(Missing) 11857
 
0.5%
ValueCountFrequency (%)
0 2036417
79.6%
0.001 526
 
< 0.1%
0.002 2
 
< 0.1%
0.003 2
 
< 0.1%
0.004 12
 
< 0.1%
0.005 3
 
< 0.1%
0.006 6
 
< 0.1%
0.007 3
 
< 0.1%
0.008 2
 
< 0.1%
0.009 1
 
< 0.1%
ValueCountFrequency (%)
4538.833 451
< 0.1%
4522.271 1
 
< 0.1%
4505.936 1
 
< 0.1%
4490.665 1
 
< 0.1%
4471.793 1
 
< 0.1%
4454.803 1
 
< 0.1%
4436.3 1
 
< 0.1%
4418.447 1
 
< 0.1%
4402.213 1
 
< 0.1%
4385.098 1
 
< 0.1%

estado
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing2553249
Missing (%)99.8%
Memory size19.5 MiB
1.0
4018 
0.0
 
389

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters13221
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 4018
 
0.2%
0.0 389
 
< 0.1%
(Missing) 2553249
99.8%

Length

2024-08-26T18:19:10.625431image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-26T18:19:10.677154image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 4018
91.2%
0.0 389
 
8.8%

Most occurring characters

ValueCountFrequency (%)
0 4796
36.3%
. 4407
33.3%
1 4018
30.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13221
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4796
36.3%
. 4407
33.3%
1 4018
30.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13221
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4796
36.3%
. 4407
33.3%
1 4018
30.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13221
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4796
36.3%
. 4407
33.3%
1 4018
30.4%

resultado_del_ensayo
Categorical

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)0.2%
Missing2551416
Missing (%)99.8%
Memory size19.5 MiB
No Aplicable
2219 
Acuifera
1392 
Petróleo
1059 
Capa Seca
730 
MEMORY
656 
Other values (5)
 
184

Length

Max length20
Median length17
Mean length9.2269231
Min length4

Characters and Unicode

Total characters57576
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowPetróleo
2nd rowPetróleo
3rd rowPetróleo
4th rowCapa Seca
5th rowPetróleo

Common Values

ValueCountFrequency (%)
No Aplicable 2219
 
0.1%
Acuifera 1392
 
0.1%
Petróleo 1059
 
< 0.1%
Capa Seca 730
 
< 0.1%
MEMORY 656
 
< 0.1%
DFIT 176
 
< 0.1%
Surgente Acuifera 3
 
< 0.1%
Capa cementada 3
 
< 0.1%
Surgente petrolífera 1
 
< 0.1%
Petroleo con agua 1
 
< 0.1%
(Missing) 2551416
99.8%

Length

2024-08-26T18:19:10.743154image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-26T18:19:10.809155image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
no 2219
24.1%
aplicable 2219
24.1%
acuifera 1395
15.2%
petróleo 1059
11.5%
capa 733
 
8.0%
seca 730
 
7.9%
memory 656
 
7.1%
dfit 176
 
1.9%
surgente 4
 
< 0.1%
cementada 3
 
< 0.1%
Other values (4) 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 6480
 
11.3%
a 5819
 
10.1%
l 5499
 
9.6%
c 4348
 
7.6%
i 3614
 
6.3%
A 3614
 
6.3%
o 3282
 
5.7%
2958
 
5.1%
p 2953
 
5.1%
r 2461
 
4.3%
Other values (23) 16548
28.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 57576
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 6480
 
11.3%
a 5819
 
10.1%
l 5499
 
9.6%
c 4348
 
7.6%
i 3614
 
6.3%
A 3614
 
6.3%
o 3282
 
5.7%
2958
 
5.1%
p 2953
 
5.1%
r 2461
 
4.3%
Other values (23) 16548
28.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 57576
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 6480
 
11.3%
a 5819
 
10.1%
l 5499
 
9.6%
c 4348
 
7.6%
i 3614
 
6.3%
A 3614
 
6.3%
o 3282
 
5.7%
2958
 
5.1%
p 2953
 
5.1%
r 2461
 
4.3%
Other values (23) 16548
28.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 57576
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 6480
 
11.3%
a 5819
 
10.1%
l 5499
 
9.6%
c 4348
 
7.6%
i 3614
 
6.3%
A 3614
 
6.3%
o 3282
 
5.7%
2958
 
5.1%
p 2953
 
5.1%
r 2461
 
4.3%
Other values (23) 16548
28.7%

test_caudal_de_petroleo_[l/h]
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct456
Distinct (%)18.5%
Missing2555186
Missing (%)99.9%
Infinite0
Infinite (%)0.0%
Mean234.70912
Minimum0
Maximum4953.5
Zeros1391
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size19.5 MiB
2024-08-26T18:19:10.892154image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3240
95-th percentile1224.17
Maximum4953.5
Range4953.5
Interquartile range (IQR)240

Descriptive statistics

Standard deviation492.73041
Coefficient of variation (CV)2.0993236
Kurtosis16.543185
Mean234.70912
Median Absolute Deviation (MAD)0
Skewness3.460497
Sum579731.53
Variance242783.25
MonotonicityNot monotonic
2024-08-26T18:19:10.964154image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1391
 
0.1%
240 27
 
< 0.1%
120 17
 
< 0.1%
1293.75 17
 
< 0.1%
1152.08 15
 
< 0.1%
810 15
 
< 0.1%
960 14
 
< 0.1%
720 12
 
< 0.1%
19.17 11
 
< 0.1%
215.83 11
 
< 0.1%
Other values (446) 940
 
< 0.1%
(Missing) 2555186
99.9%
ValueCountFrequency (%)
0 1391
0.1%
0.42 10
 
< 0.1%
0.83 7
 
< 0.1%
1.25 1
 
< 0.1%
1.67 1
 
< 0.1%
2.08 2
 
< 0.1%
2.92 1
 
< 0.1%
3.33 5
 
< 0.1%
4.17 1
 
< 0.1%
4.58 2
 
< 0.1%
ValueCountFrequency (%)
4953.5 2
< 0.1%
3871.67 2
< 0.1%
3567.29 1
< 0.1%
3324.06 1
< 0.1%
3315.32 2
< 0.1%
3271.25 1
< 0.1%
2998.18 1
< 0.1%
2832.92 1
< 0.1%
2747 1
< 0.1%
2661.16 1
< 0.1%

test_caudal_de_agua_[l/h]
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct679
Distinct (%)27.5%
Missing2555186
Missing (%)99.9%
Infinite0
Infinite (%)0.0%
Mean814.04036
Minimum0
Maximum5928.3
Zeros11
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size19.5 MiB
2024-08-26T18:19:11.033007image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile55.69
Q1180
median414
Q31220
95-th percentile2400
Maximum5928.3
Range5928.3
Interquartile range (IQR)1040

Descriptive statistics

Standard deviation889.15407
Coefficient of variation (CV)1.0922727
Kurtosis2.9851999
Mean814.04036
Median Absolute Deviation (MAD)294.4
Skewness1.6146739
Sum2010679.7
Variance790594.96
MonotonicityNot monotonic
2024-08-26T18:19:11.102007image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2400 149
 
< 0.1%
1800 108
 
< 0.1%
300 42
 
< 0.1%
1200 41
 
< 0.1%
200 38
 
< 0.1%
400 33
 
< 0.1%
100 32
 
< 0.1%
600 28
 
< 0.1%
180 28
 
< 0.1%
150 27
 
< 0.1%
Other values (669) 1944
 
0.1%
(Missing) 2555186
99.9%
ValueCountFrequency (%)
0 11
< 0.1%
0.4 2
 
< 0.1%
0.5 2
 
< 0.1%
0.8 2
 
< 0.1%
2.9 1
 
< 0.1%
3.3 1
 
< 0.1%
6.7 1
 
< 0.1%
7.1 1
 
< 0.1%
8.8 3
 
< 0.1%
9.6 1
 
< 0.1%
ValueCountFrequency (%)
5928.3 2
< 0.1%
5537.5 1
< 0.1%
5327.9 2
< 0.1%
5317.9 1
< 0.1%
5166.5 1
< 0.1%
4995 2
< 0.1%
4916.7 2
< 0.1%
4853.8 1
< 0.1%
4387.6 1
< 0.1%
4133.3 2
< 0.1%

test_caudal_de_liquido_[l/h]
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct482
Distinct (%)13.5%
Missing2554075
Missing (%)99.9%
Infinite0
Infinite (%)0.0%
Mean1483.1438
Minimum0.8
Maximum5991.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.5 MiB
2024-08-26T18:19:11.169006image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0.8
5-th percentile135.8
Q1400
median1600
Q32400
95-th percentile3600
Maximum5991.7
Range5990.9
Interquartile range (IQR)2000

Descriptive statistics

Standard deviation1099.7971
Coefficient of variation (CV)0.74153098
Kurtosis-0.58574611
Mean1483.1438
Median Absolute Deviation (MAD)1000
Skewness0.43639428
Sum5311138.1
Variance1209553.7
MonotonicityNot monotonic
2024-08-26T18:19:11.243007image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2400 657
 
< 0.1%
1800 472
 
< 0.1%
3000 218
 
< 0.1%
3600 146
 
< 0.1%
1200 95
 
< 0.1%
300 58
 
< 0.1%
200 51
 
< 0.1%
600 48
 
< 0.1%
400 48
 
< 0.1%
2700 40
 
< 0.1%
Other values (472) 1748
 
0.1%
(Missing) 2554075
99.9%
ValueCountFrequency (%)
0.8 1
 
< 0.1%
2.9 2
 
< 0.1%
11.3 1
 
< 0.1%
12.9 1
 
< 0.1%
14.2 1
 
< 0.1%
34.2 1
 
< 0.1%
37.1 1
 
< 0.1%
40 1
 
< 0.1%
50 1
 
< 0.1%
60 5
< 0.1%
ValueCountFrequency (%)
5991.7 1
 
< 0.1%
5928.3 2
 
< 0.1%
5796.7 2
 
< 0.1%
5500.4 2
 
< 0.1%
5318.3 1
 
< 0.1%
5292.5 2
 
< 0.1%
5214.2 2
 
< 0.1%
5166.5 1
 
< 0.1%
4982.9 1
 
< 0.1%
4500 5
< 0.1%

test_qwi_[l/h]
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct311
Distinct (%)32.1%
Missing2556686
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean478.78993
Minimum0
Maximum13200
Zeros68
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size19.5 MiB
2024-08-26T18:19:11.321007image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1228.92
median388.8
Q3633.6
95-th percentile923.616
Maximum13200
Range13200
Interquartile range (IQR)404.68

Descriptive statistics

Standard deviation781.56967
Coefficient of variation (CV)1.6323854
Kurtosis216.1853
Mean478.78993
Median Absolute Deviation (MAD)198.72
Skewness13.530562
Sum464426.23
Variance610851.15
MonotonicityNot monotonic
2024-08-26T18:19:11.400007image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 68
 
< 0.1%
158.4 31
 
< 0.1%
475.2 25
 
< 0.1%
316.8 19
 
< 0.1%
244.8 19
 
< 0.1%
331.2 15
 
< 0.1%
328.32 12
 
< 0.1%
457.83 12
 
< 0.1%
864 11
 
< 0.1%
259.2 11
 
< 0.1%
Other values (301) 747
 
< 0.1%
(Missing) 2556686
> 99.9%
ValueCountFrequency (%)
0 68
< 0.1%
1.63 1
 
< 0.1%
3.37 2
 
< 0.1%
4.56 1
 
< 0.1%
4.8 1
 
< 0.1%
5.28 3
 
< 0.1%
5.52 1
 
< 0.1%
5.72 2
 
< 0.1%
6.87 1
 
< 0.1%
7.92 1
 
< 0.1%
ValueCountFrequency (%)
13200 3
< 0.1%
3412.8 2
< 0.1%
2410.56 1
 
< 0.1%
2276.64 1
 
< 0.1%
1872 1
 
< 0.1%
1820.16 1
 
< 0.1%
1771.2 1
 
< 0.1%
1512 1
 
< 0.1%
1504.8 1
 
< 0.1%
1360.94 4
< 0.1%

test_porcentaje_de_agua_[%]
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct146
Distinct (%)5.9%
Missing2555186
Missing (%)99.9%
Infinite0
Infinite (%)0.0%
Mean79.11336
Minimum0
Maximum100
Zeros11
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size19.5 MiB
2024-08-26T18:19:11.475007image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.9
Q164.25
median100
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)35.75

Descriptive statistics

Standard deviation31.637743
Coefficient of variation (CV)0.39990393
Kurtosis-0.058787336
Mean79.11336
Median Absolute Deviation (MAD)0
Skewness-1.2265905
Sum195410
Variance1000.9468
MonotonicityNot monotonic
2024-08-26T18:19:11.548007image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 1392
 
0.1%
30 74
 
< 0.1%
20 49
 
< 0.1%
90 44
 
< 0.1%
28 41
 
< 0.1%
70 40
 
< 0.1%
80 26
 
< 0.1%
40 26
 
< 0.1%
85 22
 
< 0.1%
95 20
 
< 0.1%
Other values (136) 736
 
< 0.1%
(Missing) 2555186
99.9%
ValueCountFrequency (%)
0 11
< 0.1%
0.1 6
 
< 0.1%
0.5 1
 
< 0.1%
1 13
< 0.1%
1.6 1
 
< 0.1%
2 6
 
< 0.1%
3 3
 
< 0.1%
4 20
< 0.1%
5 15
< 0.1%
6 12
< 0.1%
ValueCountFrequency (%)
100 1392
0.1%
99.9 6
 
< 0.1%
99.8 6
 
< 0.1%
99.7 7
 
< 0.1%
99.6 1
 
< 0.1%
99.5 5
 
< 0.1%
99.3 1
 
< 0.1%
99 2
 
< 0.1%
98.9 1
 
< 0.1%
98.7 3
 
< 0.1%

test_nivel_[m tvd]
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct786
Distinct (%)22.4%
Missing2554153
Missing (%)99.9%
Infinite0
Infinite (%)0.0%
Mean1517.7368
Minimum0
Maximum2583
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size19.5 MiB
2024-08-26T18:19:11.617062image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile400
Q11075
median1645
Q32000
95-th percentile2324
Maximum2583
Range2583
Interquartile range (IQR)925

Descriptive statistics

Standard deviation599.48341
Coefficient of variation (CV)0.3949851
Kurtosis-0.79576229
Mean1517.7368
Median Absolute Deviation (MAD)445
Skewness-0.4975785
Sum5316632
Variance359380.36
MonotonicityNot monotonic
2024-08-26T18:19:11.694062image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1200 68
 
< 0.1%
900 66
 
< 0.1%
1100 66
 
< 0.1%
1400 62
 
< 0.1%
1000 52
 
< 0.1%
950 50
 
< 0.1%
750 45
 
< 0.1%
400 45
 
< 0.1%
600 43
 
< 0.1%
1300 40
 
< 0.1%
Other values (776) 2966
 
0.1%
(Missing) 2554153
99.9%
ValueCountFrequency (%)
0 2
 
< 0.1%
90 2
 
< 0.1%
120 6
 
< 0.1%
170 5
 
< 0.1%
175 2
 
< 0.1%
196 3
 
< 0.1%
200 16
< 0.1%
230 2
 
< 0.1%
240 7
< 0.1%
250 16
< 0.1%
ValueCountFrequency (%)
2583 1
< 0.1%
2578 1
< 0.1%
2569 1
< 0.1%
2526 2
< 0.1%
2511 1
< 0.1%
2500 1
< 0.1%
2498 1
< 0.1%
2490 2
< 0.1%
2486 1
< 0.1%
2480 2
< 0.1%

test_salinidad_[g/cm3]
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)1.2%
Missing2555749
Missing (%)99.9%
Infinite0
Infinite (%)0.0%
Mean0.006311484
Minimum0
Maximum0.037
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size19.5 MiB
2024-08-26T18:19:11.761062image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.002
Q10.004
median0.006
Q30.008
95-th percentile0.0117
Maximum0.037
Range0.037
Interquartile range (IQR)0.004

Descriptive statistics

Standard deviation0.0030647934
Coefficient of variation (CV)0.48558997
Kurtosis6.6844362
Mean0.006311484
Median Absolute Deviation (MAD)0.002
Skewness1.4296463
Sum12.036
Variance9.3929583 × 10-6
MonotonicityNot monotonic
2024-08-26T18:19:11.823062image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0.005 311
 
< 0.1%
0.004 309
 
< 0.1%
0.006 199
 
< 0.1%
0.003 198
 
< 0.1%
0.009 196
 
< 0.1%
0.008 179
 
< 0.1%
0.007 158
 
< 0.1%
0.01 122
 
< 0.1%
0.002 87
 
< 0.1%
0.011 35
 
< 0.1%
Other values (13) 113
 
< 0.1%
(Missing) 2555749
99.9%
ValueCountFrequency (%)
0 2
 
< 0.1%
0.001 15
 
< 0.1%
0.002 87
 
< 0.1%
0.003 198
< 0.1%
0.004 309
< 0.1%
0.005 311
< 0.1%
0.006 199
< 0.1%
0.007 158
< 0.1%
0.008 179
< 0.1%
0.009 196
< 0.1%
ValueCountFrequency (%)
0.037 1
 
< 0.1%
0.026 1
 
< 0.1%
0.02 2
 
< 0.1%
0.019 3
 
< 0.1%
0.018 1
 
< 0.1%
0.017 5
 
< 0.1%
0.016 7
 
< 0.1%
0.015 2
 
< 0.1%
0.014 18
< 0.1%
0.013 32
< 0.1%

test_pws_[kg/cm2]
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct406
Distinct (%)62.2%
Missing2557003
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean240.14533
Minimum5.5
Maximum405
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.5 MiB
2024-08-26T18:19:11.890062image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum5.5
5-th percentile116.22
Q1190
median239.8
Q3300.6
95-th percentile339.98
Maximum405
Range399.5
Interquartile range (IQR)110.6

Descriptive statistics

Standard deviation72.723258
Coefficient of variation (CV)0.3028302
Kurtosis0.043754776
Mean240.14533
Median Absolute Deviation (MAD)54.6
Skewness-0.50679238
Sum156814.9
Variance5288.6723
MonotonicityNot monotonic
2024-08-26T18:19:11.966567image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
286.9 6
 
< 0.1%
329.3 5
 
< 0.1%
175.8 5
 
< 0.1%
201.1 5
 
< 0.1%
183.7 5
 
< 0.1%
338.9 5
 
< 0.1%
273.9 5
 
< 0.1%
246.6 5
 
< 0.1%
193.3 4
 
< 0.1%
323.4 4
 
< 0.1%
Other values (396) 604
 
< 0.1%
(Missing) 2557003
> 99.9%
ValueCountFrequency (%)
5.5 3
< 0.1%
10.6 1
 
< 0.1%
14.1 2
< 0.1%
26.2 1
 
< 0.1%
31.6 1
 
< 0.1%
45 1
 
< 0.1%
51.3 1
 
< 0.1%
56.7 1
 
< 0.1%
57.9 1
 
< 0.1%
70.2 1
 
< 0.1%
ValueCountFrequency (%)
405 1
< 0.1%
378.7 1
< 0.1%
374.2 1
< 0.1%
368.6 1
< 0.1%
367.7 1
< 0.1%
365.3 2
< 0.1%
363 1
< 0.1%
362.5 1
< 0.1%
361.7 2
< 0.1%
360.7 1
< 0.1%

test_ptub_[kg/cm2]
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)33.3%
Missing2557647
Missing (%)> 99.9%
Memory size19.5 MiB
0.0
21.1
105.5

Length

Max length5
Median length3
Mean length3.4444444
Min length3

Characters and Unicode

Total characters31
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)11.1%

Sample

1st row21.1
2nd row21.1
3rd row105.5
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 6
 
< 0.1%
21.1 2
 
< 0.1%
105.5 1
 
< 0.1%
(Missing) 2557647
> 99.9%

Length

2024-08-26T18:19:12.043567image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-26T18:19:12.105568image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 6
66.7%
21.1 2
 
22.2%
105.5 1
 
11.1%

Most occurring characters

ValueCountFrequency (%)
0 13
41.9%
. 9
29.0%
1 5
 
16.1%
2 2
 
6.5%
5 2
 
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 31
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 13
41.9%
. 9
29.0%
1 5
 
16.1%
2 2
 
6.5%
5 2
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 31
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 13
41.9%
. 9
29.0%
1 5
 
16.1%
2 2
 
6.5%
5 2
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 31
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 13
41.9%
. 9
29.0%
1 5
 
16.1%
2 2
 
6.5%
5 2
 
6.5%

test_presion_de_inyeccion_[kg/cm2]
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct87
Distinct (%)8.9%
Missing2556682
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean134.62423
Minimum18.3
Maximum421.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.5 MiB
2024-08-26T18:19:12.171566image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum18.3
5-th percentile70.3
Q1105.5
median126.6
Q3161.7
95-th percentile210.9
Maximum421.8
Range403.5
Interquartile range (IQR)56.2

Descriptive statistics

Standard deviation43.673379
Coefficient of variation (CV)0.3244095
Kurtosis4.5841003
Mean134.62423
Median Absolute Deviation (MAD)28.1
Skewness1.0908808
Sum131124
Variance1907.364
MonotonicityNot monotonic
2024-08-26T18:19:12.252567image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
119.5 131
 
< 0.1%
140.6 57
 
< 0.1%
105.5 53
 
< 0.1%
98.4 53
 
< 0.1%
126.6 51
 
< 0.1%
84.4 39
 
< 0.1%
210.9 35
 
< 0.1%
112.5 32
 
< 0.1%
91.4 31
 
< 0.1%
147.6 30
 
< 0.1%
Other values (77) 462
 
< 0.1%
(Missing) 2556682
> 99.9%
ValueCountFrequency (%)
18.3 1
 
< 0.1%
28.1 1
 
< 0.1%
45.7 2
 
< 0.1%
49.2 6
 
< 0.1%
56.3 9
< 0.1%
59.8 2
 
< 0.1%
63.3 8
 
< 0.1%
70.3 21
< 0.1%
73.8 2
 
< 0.1%
75.2 1
 
< 0.1%
ValueCountFrequency (%)
421.8 3
 
< 0.1%
211 24
< 0.1%
210.9 35
< 0.1%
210 1
 
< 0.1%
208 1
 
< 0.1%
207.4 6
 
< 0.1%
204 10
 
< 0.1%
203.9 4
 
< 0.1%
200.4 6
 
< 0.1%
197 11
 
< 0.1%

estimulacion
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing2786
Missing (%)0.1%
Memory size19.5 MiB
0.0
2552764 
1.0
 
2106

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7664610
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 2552764
99.8%
1.0 2106
 
0.1%
(Missing) 2786
 
0.1%

Length

2024-08-26T18:19:12.320567image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-26T18:19:12.366567image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2552764
99.9%
1.0 2106
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 5107634
66.6%
. 2554870
33.3%
1 2106
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7664610
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5107634
66.6%
. 2554870
33.3%
1 2106
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7664610
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5107634
66.6%
. 2554870
33.3%
1 2106
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7664610
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5107634
66.6%
. 2554870
33.3%
1 2106
 
< 0.1%

memory_[%]
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct95
Distinct (%)35.8%
Missing2557391
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean118.0566
Minimum0
Maximum168
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size19.5 MiB
2024-08-26T18:19:12.419567image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile68
Q1100
median119
Q3140
95-th percentile160
Maximum168
Range168
Interquartile range (IQR)40

Descriptive statistics

Standard deviation30.192071
Coefficient of variation (CV)0.25574233
Kurtosis1.723671
Mean118.0566
Median Absolute Deviation (MAD)20
Skewness-0.89900135
Sum31285
Variance911.56118
MonotonicityNot monotonic
2024-08-26T18:19:12.492659image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
124 8
 
< 0.1%
118 8
 
< 0.1%
132 7
 
< 0.1%
102 7
 
< 0.1%
106 6
 
< 0.1%
109 6
 
< 0.1%
154 6
 
< 0.1%
155 6
 
< 0.1%
129 5
 
< 0.1%
110 5
 
< 0.1%
Other values (85) 201
 
< 0.1%
(Missing) 2557391
> 99.9%
ValueCountFrequency (%)
0 3
< 0.1%
26 1
 
< 0.1%
28 1
 
< 0.1%
48 1
 
< 0.1%
50 1
 
< 0.1%
61 2
< 0.1%
63 1
 
< 0.1%
64 1
 
< 0.1%
67 2
< 0.1%
68 2
< 0.1%
ValueCountFrequency (%)
168 1
 
< 0.1%
167 1
 
< 0.1%
166 1
 
< 0.1%
165 2
 
< 0.1%
163 3
< 0.1%
161 5
< 0.1%
160 3
< 0.1%
158 5
< 0.1%
157 2
 
< 0.1%
156 2
 
< 0.1%

h_punzado
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct42
Distinct (%)0.8%
Missing2552348
Missing (%)99.8%
Infinite0
Infinite (%)0.0%
Mean3.4122645
Minimum0.1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.5 MiB
2024-08-26T18:19:12.561659image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile1
Q12
median3
Q34.5
95-th percentile7.5
Maximum16
Range15.9
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation2.0115144
Coefficient of variation (CV)0.58949545
Kurtosis3.3192924
Mean3.4122645
Median Absolute Deviation (MAD)1
Skewness1.4992971
Sum18112.3
Variance4.0461902
MonotonicityNot monotonic
2024-08-26T18:19:12.629659image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
2 805
 
< 0.1%
3 672
 
< 0.1%
1.5 640
 
< 0.1%
2.5 571
 
< 0.1%
3.5 437
 
< 0.1%
4 424
 
< 0.1%
1 314
 
< 0.1%
4.5 297
 
< 0.1%
5 296
 
< 0.1%
6 190
 
< 0.1%
Other values (32) 662
 
< 0.1%
(Missing) 2552348
99.8%
ValueCountFrequency (%)
0.1 4
 
< 0.1%
0.1 1
 
< 0.1%
0.3 18
 
< 0.1%
0.3 9
 
< 0.1%
0.5 5
 
< 0.1%
1 314
< 0.1%
1.5 640
< 0.1%
1.5 1
 
< 0.1%
1.6 3
 
< 0.1%
1.7 2
 
< 0.1%
ValueCountFrequency (%)
16 1
 
< 0.1%
15.5 3
 
< 0.1%
14.5 1
 
< 0.1%
14 1
 
< 0.1%
13.5 2
 
< 0.1%
13 5
< 0.1%
12.5 2
 
< 0.1%
12 7
< 0.1%
11.5 10
< 0.1%
11 8
< 0.1%

sft_[kg/cm2]
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct749
Distinct (%)76.7%
Missing2556680
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean138.42111
Minimum14.3
Maximum293.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.5 MiB
2024-08-26T18:19:12.698662image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum14.3
5-th percentile53.625
Q1102.55
median137.75
Q3171.875
95-th percentile217.675
Maximum293.4
Range279.1
Interquartile range (IQR)69.325

Descriptive statistics

Standard deviation49.618228
Coefficient of variation (CV)0.35845854
Kurtosis-0.29134808
Mean138.42111
Median Absolute Deviation (MAD)34.55
Skewness0.035491249
Sum135099
Variance2461.9686
MonotonicityNot monotonic
2024-08-26T18:19:12.765660image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
147.2 5
 
< 0.1%
77.1 5
 
< 0.1%
119 4
 
< 0.1%
127.7 4
 
< 0.1%
133.1 4
 
< 0.1%
142.2 4
 
< 0.1%
165.7 4
 
< 0.1%
159.2 4
 
< 0.1%
138.6 3
 
< 0.1%
146.3 3
 
< 0.1%
Other values (739) 936
 
< 0.1%
(Missing) 2556680
> 99.9%
ValueCountFrequency (%)
14.3 1
< 0.1%
18.1 1
< 0.1%
20.9 1
< 0.1%
23.8 1
< 0.1%
23.9 1
< 0.1%
24 1
< 0.1%
24.4 1
< 0.1%
25.6 1
< 0.1%
26.8 1
< 0.1%
27.9 1
< 0.1%
ValueCountFrequency (%)
293.4 1
< 0.1%
293.3 1
< 0.1%
281.3 1
< 0.1%
270.1 1
< 0.1%
269.7 1
< 0.1%
263.6 1
< 0.1%
257.8 1
< 0.1%
256.7 1
< 0.1%
248 1
< 0.1%
244.1 1
< 0.1%

Interactions

2024-08-26T18:18:57.041413image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
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2024-08-26T18:18:07.908459image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:11.462917image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:15.018091image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:18.626232image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:22.172442image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:26.637923image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:30.134065image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:33.867574image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:37.531575image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:41.145314image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
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2024-08-26T18:18:46.678102image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:48.059066image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:49.673958image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:51.062536image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:52.420545image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:53.727293image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:55.030497image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:56.669413image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:58.108664image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:07.410145image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:10.947665image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:14.513953image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:18.109674image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:21.665442image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:26.093924image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:29.636013image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:33.187468image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:36.994086image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:40.834334image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:42.235472image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:43.956900image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:45.403991image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:46.745215image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:48.129065image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:49.742465image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:51.135535image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:52.477545image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:53.791499image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:55.083401image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:56.739412image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:58.164663image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:07.466096image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:11.006666image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:14.569955image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:18.166567image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:21.722444image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:26.152923image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:29.690010image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:33.259240image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:37.050361image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:40.896246image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:42.314924image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:44.016064image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:45.470302image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:46.804964image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:48.190066image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:49.796465image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:51.198535image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:52.547545image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:53.839555image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:55.138289image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:56.799412image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:58.221664image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:07.519022image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:11.058912image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:14.625955image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:18.219070image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:21.776442image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:26.210925image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:29.742414image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:33.313630image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:37.106091image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:40.951958image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:42.378168image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:44.082010image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:45.541701image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:46.890022image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:48.272065image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:49.860465image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:51.278535image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:52.601390image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:53.917799image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:55.185314image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:56.865412image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:58.291664image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:07.578087image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:11.118920image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:14.685954image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:18.289901image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:21.836443image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:26.283922image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:29.802529image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:33.536022image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:37.167541image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:41.016212image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:42.433081image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:44.138208image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:45.597089image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:46.948164image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:48.327571image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:49.914465image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:51.335535image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:52.657390image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:53.972425image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:55.258354image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:56.912412image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:58.342664image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:07.636111image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:11.181919image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:14.750954image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:18.351450image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:21.896442image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:26.343922image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:29.861085image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:33.595032image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:37.230117image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:41.082303image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:42.503916image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:44.207080image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:45.667921image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:47.018057image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:48.393571image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:49.980483image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:51.406535image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:52.716390image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:54.037569image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:55.330453image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-26T18:18:56.982413image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Correlations

2024-08-26T18:19:12.828659image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
estadoestimulaciongp_[mmm3]h_punzadolp_[mm3]memory_[%]np_[mm3]qg_[m3/dc]ql_[m3/dc]qo_[m3/dc]qw_[m3/dc]qwi_[m3/dc]resultado_del_ensayosft_[kg/cm2]test_caudal_de_agua_[l/h]test_caudal_de_liquido_[l/h]test_caudal_de_petroleo_[l/h]test_nivel_[m tvd]test_porcentaje_de_agua_[%]test_presion_de_inyeccion_[kg/cm2]test_ptub_[kg/cm2]test_pws_[kg/cm2]test_qwi_[l/h]test_salinidad_[g/cm3]wi_[mm3]wp_[mm3]
estado1.0000.0110.0930.0000.1190.0000.1900.0000.0970.1530.0590.0591.0001.0001.0001.0001.0001.0001.0001.0000.0000.0001.0001.0000.0810.110
estimulacion0.0111.0000.0000.3380.0001.0000.0000.0000.0000.0000.0000.0000.3951.0000.0970.3540.1320.3500.2480.1390.0000.3330.1740.1250.0000.000
gp_[mmm3]0.0930.0001.000-0.4580.3040.0810.3040.086-0.0030.0020.043-0.1941.000-0.0260.3030.705-0.116-0.3550.116NaN0.000-0.431NaN-0.386-0.5070.197
h_punzado0.0000.338-0.4581.0000.301NaN0.3360.3880.3010.3360.0250.1930.160NaN0.1030.064-0.178-0.1440.217-0.1610.7070.2060.2440.1390.1930.025
lp_[mm3]0.1190.0000.3040.3011.0000.0560.9010.0430.3720.3540.236-0.0951.0000.0240.098-0.184-0.1250.1430.125NaN0.000-0.355NaN-0.242-0.2610.541
memory_[%]0.0001.0000.081NaN0.0561.0000.056NaNNaNNaNNaN0.0530.000NaNNaNNaNNaNNaNNaNNaN0.0000.708NaNNaN-0.067NaN
np_[mm3]0.1900.0000.3040.3360.9010.0561.0000.0450.3730.4060.141-0.0821.0000.019-0.107-0.3720.0070.052-0.007NaN0.000-0.258NaN0.013-0.2300.259
qg_[m3/dc]0.0000.0000.0860.3880.043NaN0.0451.0000.5170.4860.287-0.0821.0000.0790.052-0.2970.0270.319-0.027NaN0.000-0.443NaN-0.428-0.2130.028
ql_[m3/dc]0.0970.000-0.0030.3010.372NaN0.3730.5171.0000.9600.496-0.0731.0000.0290.101-0.200-0.0920.1090.092NaN0.000-0.362NaN-0.310-0.1910.125
qo_[m3/dc]0.1530.0000.0020.3360.354NaN0.4060.4860.9601.0000.350-0.0701.0000.019-0.129-0.3980.0470.018-0.047NaN0.000-0.269NaN-0.013-0.1840.056
qw_[m3/dc]0.0590.0000.0430.0250.236NaN0.1410.2870.4960.3501.000-0.0331.000-0.0180.0940.2540.007-0.067-0.007NaN0.000-0.362NaN-0.423-0.0860.418
qwi_[m3/dc]0.0590.000-0.1940.193-0.0950.053-0.082-0.082-0.073-0.070-0.0331.0001.000-0.167NaNNaNNaNNaNNaNNaN0.000-0.007NaNNaN0.370-0.046
resultado_del_ensayo1.0000.3951.0000.1601.0000.0001.0001.0001.0001.0001.0001.0001.0000.0000.1840.3460.3010.4020.4060.2240.0000.4920.0500.0611.0001.000
sft_[kg/cm2]1.0001.000-0.026NaN0.024NaN0.0190.0790.0290.019-0.018-0.1670.0001.000NaNNaNNaNNaNNaNNaN0.000NaNNaNNaN-0.167-0.018
test_caudal_de_agua_[l/h]1.0000.0970.3030.1030.098NaN-0.1070.0520.101-0.1290.094NaN0.184NaN1.0000.801-0.175-0.5290.333NaN1.000-1.000NaN-0.265NaN0.094
test_caudal_de_liquido_[l/h]1.0000.3540.7050.064-0.184NaN-0.372-0.297-0.200-0.3980.254NaN0.346NaN0.8011.0000.313-0.801-0.172NaN1.000-1.000NaN-0.267NaN0.254
test_caudal_de_petroleo_[l/h]1.0000.132-0.116-0.178-0.125NaN0.0070.027-0.0920.0470.007NaN0.301NaN-0.1750.3131.000-0.176-0.964NaN1.000NaNNaN-0.030NaN0.007
test_nivel_[m tvd]1.0000.350-0.355-0.1440.143NaN0.0520.3190.1090.018-0.067NaN0.402NaN-0.529-0.801-0.1761.0000.082NaN1.000NaN1.0000.193NaN-0.067
test_porcentaje_de_agua_[%]1.0000.2480.1160.2170.125NaN-0.007-0.0270.092-0.047-0.007NaN0.406NaN0.333-0.172-0.9640.0821.000NaN1.000NaNNaN0.009NaN-0.007
test_presion_de_inyeccion_[kg/cm2]1.0000.139NaN-0.161NaNNaNNaNNaNNaNNaNNaNNaN0.224NaNNaNNaNNaNNaNNaN1.0000.000NaN-0.444NaNNaNNaN
test_ptub_[kg/cm2]0.0000.0000.0000.7070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.0001.0001.0001.0000.0001.0000.8940.0001.0000.0000.000
test_pws_[kg/cm2]0.0000.333-0.4310.206-0.3550.708-0.258-0.443-0.362-0.269-0.362-0.0070.492NaN-1.000-1.000NaNNaNNaNNaN0.8941.000NaN1.000-0.107-0.362
test_qwi_[l/h]1.0000.174NaN0.244NaNNaNNaNNaNNaNNaNNaNNaN0.050NaNNaNNaNNaN1.000NaN-0.4440.000NaN1.000NaNNaNNaN
test_salinidad_[g/cm3]1.0000.125-0.3860.139-0.242NaN0.013-0.428-0.310-0.013-0.423NaN0.061NaN-0.265-0.267-0.0300.1930.009NaN1.0001.000NaN1.000NaN-0.423
wi_[mm3]0.0810.000-0.5070.193-0.261-0.067-0.230-0.213-0.191-0.184-0.0860.3701.000-0.167NaNNaNNaNNaNNaNNaN0.000-0.107NaNNaN1.000-0.117
wp_[mm3]0.1100.0000.1970.0250.541NaN0.2590.0280.1250.0560.418-0.0461.000-0.0180.0940.2540.007-0.067-0.007NaN0.000-0.362NaN-0.423-0.1171.000

Missing values

2024-08-26T18:18:58.580298image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-26T18:19:00.504104image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-08-26T18:19:06.339064image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

identificadorcapafechaqo_[m3/dc]np_[mm3]qg_[m3/dc]gp_[mmm3]qw_[m3/dc]wp_[mm3]ql_[m3/dc]lp_[mm3]qwi_[m3/dc]wi_[mm3]estadoresultado_del_ensayotest_caudal_de_petroleo_[l/h]test_caudal_de_agua_[l/h]test_caudal_de_liquido_[l/h]test_qwi_[l/h]test_porcentaje_de_agua_[%]test_nivel_[m tvd]test_salinidad_[g/cm3]test_pws_[kg/cm2]test_ptub_[kg/cm2]test_presion_de_inyeccion_[kg/cm2]estimulacionmemory_[%]h_punzadosft_[kg/cm2]
0PLM-33F-91961-12-01NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.000NaNNaNNaN
1PLM-33F-91961-12-04NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNPetróleo250.38027.800278.200NaN10.0001775.000NaNNaNNaNNaN0.000NaN1.800NaN
2PLM-33F-91965-01-010.0000.0007.4330.0000.0000.0000.0000.0000.0000.000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.000NaNNaNNaN
3PLM-33F-91965-02-010.0000.0005.4870.0000.0000.0000.0000.0000.0000.000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.000NaNNaNNaN
4PLM-33F-91965-03-010.0000.0004.9560.0010.0000.0000.0000.0000.0000.000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.000NaNNaNNaN
5PLM-33F-91965-04-010.0000.0005.1210.0010.0000.0000.0000.0000.0000.000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.000NaNNaNNaN
6PLM-33F-91965-05-010.0000.0004.9560.0010.0000.0000.0000.0000.0000.000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.000NaNNaNNaN
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identificadorcapafechaqo_[m3/dc]np_[mm3]qg_[m3/dc]gp_[mmm3]qw_[m3/dc]wp_[mm3]ql_[m3/dc]lp_[mm3]qwi_[m3/dc]wi_[mm3]estadoresultado_del_ensayotest_caudal_de_petroleo_[l/h]test_caudal_de_agua_[l/h]test_caudal_de_liquido_[l/h]test_qwi_[l/h]test_porcentaje_de_agua_[%]test_nivel_[m tvd]test_salinidad_[g/cm3]test_pws_[kg/cm2]test_ptub_[kg/cm2]test_presion_de_inyeccion_[kg/cm2]estimulacionmemory_[%]h_punzadosft_[kg/cm2]
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2557648PLMS.a-928J-1BC2013-04-28NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNo AplicableNaNNaN2400.000NaNNaN950.000NaNNaNNaNNaN1.000NaNNaNNaN
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Most frequently occurring

identificadorcapafechaqo_[m3/dc]np_[mm3]qg_[m3/dc]gp_[mmm3]qw_[m3/dc]wp_[mm3]ql_[m3/dc]lp_[mm3]qwi_[m3/dc]wi_[mm3]estadoresultado_del_ensayotest_caudal_de_petroleo_[l/h]test_caudal_de_agua_[l/h]test_caudal_de_liquido_[l/h]test_qwi_[l/h]test_porcentaje_de_agua_[%]test_nivel_[m tvd]test_salinidad_[g/cm3]test_pws_[kg/cm2]test_ptub_[kg/cm2]test_presion_de_inyeccion_[kg/cm2]estimulacionmemory_[%]h_punzadosft_[kg/cm2]# duplicates
157PLMS-814NaN1992-09-29NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.500NaN17
82PLMS-706NaN1982-05-22NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.500NaN12
83PLMS-706NaN1982-05-22NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2.000NaN11
158PLMS-814NaN1992-09-29NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2.000NaN9
42PLMS-27NaN1971-05-13NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.500NaN8
243PLMS-974(d)NaN2018-02-13NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2.000NaN7
47PLMS-27NaN1985-12-24NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.500NaN6
155PLMS-812NaN2005-08-13NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.000NaN6
168PLMS-816H-71993-01-11NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNCapa SecaNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.000NaN1.500NaN6
21PLM-909NaN2016-11-27NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.000NaN5